Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations17,953
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.5 MiB
Average record size in memory848.6 B

Variable types

Text1
DateTime2
Categorical5
Numeric13

Alerts

price_original has 252 (1.4%) zerosZeros
price_discounted has 9961 (55.5%) zerosZeros

Reproduction

Analysis started2025-11-16 09:51:44.722355
Analysis finished2025-11-16 09:52:02.682004
Duration17.96 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct333
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2025-11-16T16:52:03.046864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length31
Median length23
Mean length12.776305
Min length6

Characters and Unicode

Total characters229,373
Distinct characters110
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowHoàng Thuỷ
2nd rowSinh Diên Hồng
3rd rowPhong Phú
4th rowKính Diên Hồng
5th rowĐức Đạt
ValueCountFrequency (%)
limousine4589
 
9.8%
anh1551
 
3.3%
1263
 
2.7%
phát1179
 
2.5%
travel1048
 
2.2%
minh882
 
1.9%
tân856
 
1.8%
quang799
 
1.7%
long710
 
1.5%
hải651
 
1.4%
Other values (292)33148
71.0%
2025-11-16T16:52:03.506083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28723
 
12.5%
n23902
 
10.4%
i16081
 
7.0%
h13985
 
6.1%
u10264
 
4.5%
o8799
 
3.8%
T7960
 
3.5%
e7565
 
3.3%
g7446
 
3.2%
a7105
 
3.1%
Other values (100)97543
42.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)229373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
28723
 
12.5%
n23902
 
10.4%
i16081
 
7.0%
h13985
 
6.1%
u10264
 
4.5%
o8799
 
3.8%
T7960
 
3.5%
e7565
 
3.3%
g7446
 
3.2%
a7105
 
3.1%
Other values (100)97543
42.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)229373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
28723
 
12.5%
n23902
 
10.4%
i16081
 
7.0%
h13985
 
6.1%
u10264
 
4.5%
o8799
 
3.8%
T7960
 
3.5%
e7565
 
3.3%
g7446
 
3.2%
a7105
 
3.1%
Other values (100)97543
42.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)229373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
28723
 
12.5%
n23902
 
10.4%
i16081
 
7.0%
h13985
 
6.1%
u10264
 
4.5%
o8799
 
3.8%
T7960
 
3.5%
e7565
 
3.3%
g7446
 
3.2%
a7105
 
3.1%
Other values (100)97543
42.5%
Distinct383
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size796.6 KiB
Minimum2025-11-16 00:00:00
Maximum2025-11-16 23:59:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-16T16:52:03.614079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:03.734586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

pickup_point
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Văn phòng
8100 
Other
5023 
Bến xe
4830 

Length

Max length9
Median length6
Mean length7.0737481
Min length5

Characters and Unicode

Total characters126,995
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBến xe
2nd rowBến xe
3rd rowVăn phòng
4th rowBến xe
5th rowBến xe

Common Values

ValueCountFrequency (%)
Văn phòng8100
45.1%
Other5023
28.0%
Bến xe4830
26.9%

Length

2025-11-16T16:52:03.845175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T16:52:03.912199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
văn8100
26.2%
phòng8100
26.2%
other5023
16.3%
bến4830
15.6%
xe4830
15.6%

Most occurring characters

ValueCountFrequency (%)
n21030
16.6%
h13123
10.3%
12930
10.2%
e9853
7.8%
V8100
 
6.4%
ă8100
 
6.4%
p8100
 
6.4%
ò8100
 
6.4%
g8100
 
6.4%
O5023
 
4.0%
Other values (5)24536
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)126995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n21030
16.6%
h13123
10.3%
12930
10.2%
e9853
7.8%
V8100
 
6.4%
ă8100
 
6.4%
p8100
 
6.4%
ò8100
 
6.4%
g8100
 
6.4%
O5023
 
4.0%
Other values (5)24536
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)126995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n21030
16.6%
h13123
10.3%
12930
10.2%
e9853
7.8%
V8100
 
6.4%
ă8100
 
6.4%
p8100
 
6.4%
ò8100
 
6.4%
g8100
 
6.4%
O5023
 
4.0%
Other values (5)24536
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)126995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n21030
16.6%
h13123
10.3%
12930
10.2%
e9853
7.8%
V8100
 
6.4%
ă8100
 
6.4%
p8100
 
6.4%
ò8100
 
6.4%
g8100
 
6.4%
O5023
 
4.0%
Other values (5)24536
19.3%
Distinct533
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size796.6 KiB
Minimum2025-11-16 00:00:00
Maximum2025-11-16 23:55:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-16T16:52:03.998190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:04.119069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

dropoff_point
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Văn phòng
7769 
Other
5861 
Bến xe
4323 

Length

Max length9
Median length6
Mean length6.9717596
Min length5

Characters and Unicode

Total characters125,164
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBến xe
2nd rowBến xe
3rd rowVăn phòng
4th rowBến xe
5th rowBến xe

Common Values

ValueCountFrequency (%)
Văn phòng7769
43.3%
Other5861
32.6%
Bến xe4323
24.1%

Length

2025-11-16T16:52:04.229881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T16:52:04.297862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
văn7769
25.9%
phòng7769
25.9%
other5861
19.5%
bến4323
14.4%
xe4323
14.4%

Most occurring characters

ValueCountFrequency (%)
n19861
15.9%
h13630
10.9%
12092
9.7%
e10184
8.1%
V7769
 
6.2%
ă7769
 
6.2%
p7769
 
6.2%
ò7769
 
6.2%
g7769
 
6.2%
O5861
 
4.7%
Other values (5)24691
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)125164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n19861
15.9%
h13630
10.9%
12092
9.7%
e10184
8.1%
V7769
 
6.2%
ă7769
 
6.2%
p7769
 
6.2%
ò7769
 
6.2%
g7769
 
6.2%
O5861
 
4.7%
Other values (5)24691
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)125164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n19861
15.9%
h13630
10.9%
12092
9.7%
e10184
8.1%
V7769
 
6.2%
ă7769
 
6.2%
p7769
 
6.2%
ò7769
 
6.2%
g7769
 
6.2%
O5861
 
4.7%
Other values (5)24691
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)125164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n19861
15.9%
h13630
10.9%
12092
9.7%
e10184
8.1%
V7769
 
6.2%
ă7769
 
6.2%
p7769
 
6.2%
ò7769
 
6.2%
g7769
 
6.2%
O5861
 
4.7%
Other values (5)24691
19.7%

price_original
Real number (ℝ)

Zeros 

Distinct99
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean333111.85
Minimum0
Maximum1200000
Zeros252
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:04.391862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile150000
Q1220000
median300000
Q3410000
95-th percentile690000
Maximum1200000
Range1200000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation159885.66
Coefficient of variation (CV)0.4799759
Kurtosis1.4009059
Mean333111.85
Median Absolute Deviation (MAD)100000
Skewness1.0002326
Sum5.980357 × 109
Variance2.5563424 × 1010
MonotonicityNot monotonic
2025-11-16T16:52:04.515872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3500001714
 
9.5%
3000001432
 
8.0%
2500001252
 
7.0%
4500001019
 
5.7%
1500001013
 
5.6%
400000824
 
4.6%
200000790
 
4.4%
190000606
 
3.4%
500000471
 
2.6%
320000462
 
2.6%
Other values (89)8370
46.6%
ValueCountFrequency (%)
0252
1.4%
700006
 
< 0.1%
750006
 
< 0.1%
800005
 
< 0.1%
10000014
 
0.1%
110000116
 
0.6%
12000082
 
0.5%
130000306
1.7%
14000094
 
0.5%
1490008
 
< 0.1%
ValueCountFrequency (%)
12000009
 
0.1%
11000001
 
< 0.1%
10000005
 
< 0.1%
9500007
 
< 0.1%
90000028
 
0.2%
8800002
 
< 0.1%
85000074
 
0.4%
800000212
1.2%
79900014
 
0.1%
7900007
 
< 0.1%

price_discounted
Real number (ℝ)

Zeros 

Distinct133
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132047.33
Minimum0
Maximum900000
Zeros9961
Zeros (%)55.5%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:04.817858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3270000
95-th percentile450000
Maximum900000
Range900000
Interquartile range (IQR)270000

Descriptive statistics

Standard deviation172912.62
Coefficient of variation (CV)1.3094745
Kurtosis0.7969025
Mean132047.33
Median Absolute Deviation (MAD)0
Skewness1.1658629
Sum2.3706458 × 109
Variance2.9898774 × 1010
MonotonicityNot monotonic
2025-11-16T16:52:04.930867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09961
55.5%
270000642
 
3.6%
300000604
 
3.4%
170000509
 
2.8%
450000399
 
2.2%
225000320
 
1.8%
350000235
 
1.3%
120000229
 
1.3%
400000220
 
1.2%
299000216
 
1.2%
Other values (123)4618
25.7%
ValueCountFrequency (%)
09961
55.5%
8000012
 
0.1%
100000100
 
0.6%
107000162
 
0.9%
10800070
 
0.4%
11200015
 
0.1%
11800090
 
0.5%
11900023
 
0.1%
120000229
 
1.3%
13000055
 
0.3%
ValueCountFrequency (%)
9000007
 
< 0.1%
8100009
 
0.1%
8000007
 
< 0.1%
75000074
0.4%
7400008
 
< 0.1%
73000014
 
0.1%
7200005
 
< 0.1%
70000099
0.6%
68000011
 
0.1%
67500013
 
0.1%

departure_date
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-11-16
2796 
2025-11-18
2689 
2025-11-17
2677 
2025-11-14
2658 
2025-11-19
2654 
Other values (3)
4479 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters179,530
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2025-11-12
2nd row2025-11-12
3rd row2025-11-12
4th row2025-11-12
5th row2025-11-12

Common Values

ValueCountFrequency (%)
2025-11-162796
15.6%
2025-11-182689
15.0%
2025-11-172677
14.9%
2025-11-142658
14.8%
2025-11-192654
14.8%
2025-11-132591
14.4%
2025-11-151313
7.3%
2025-11-12575
 
3.2%

Length

2025-11-16T16:52:05.034860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T16:52:05.122863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2025-11-162796
15.6%
2025-11-182689
15.0%
2025-11-172677
14.9%
2025-11-142658
14.8%
2025-11-192654
14.8%
2025-11-132591
14.4%
2025-11-151313
7.3%
2025-11-12575
 
3.2%

Most occurring characters

ValueCountFrequency (%)
153859
30.0%
236481
20.3%
-35906
20.0%
519266
 
10.7%
017953
 
10.0%
62796
 
1.6%
82689
 
1.5%
72677
 
1.5%
42658
 
1.5%
92654
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)179530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
153859
30.0%
236481
20.3%
-35906
20.0%
519266
 
10.7%
017953
 
10.0%
62796
 
1.6%
82689
 
1.5%
72677
 
1.5%
42658
 
1.5%
92654
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)179530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
153859
30.0%
236481
20.3%
-35906
20.0%
519266
 
10.7%
017953
 
10.0%
62796
 
1.6%
82689
 
1.5%
72677
 
1.5%
42658
 
1.5%
92654
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)179530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
153859
30.0%
236481
20.3%
-35906
20.0%
519266
 
10.7%
017953
 
10.0%
62796
 
1.6%
82689
 
1.5%
72677
 
1.5%
42658
 
1.5%
92654
 
1.5%

start_point
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
Sài Gòn
7133 
Hà Nội
6005 
Đà Nẵng
4815 

Length

Max length7
Median length7
Mean length6.6655155
Min length6

Characters and Unicode

Total characters119,666
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSài Gòn
2nd rowSài Gòn
3rd rowSài Gòn
4th rowSài Gòn
5th rowSài Gòn

Common Values

ValueCountFrequency (%)
Sài Gòn7133
39.7%
Hà Nội6005
33.4%
Đà Nẵng4815
26.8%

Length

2025-11-16T16:52:05.235467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T16:52:05.306445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sài7133
19.9%
gòn7133
19.9%
6005
16.7%
nội6005
16.7%
đà4815
13.4%
nẵng4815
13.4%

Most occurring characters

ValueCountFrequency (%)
à17953
15.0%
17953
15.0%
i13138
11.0%
n11948
10.0%
N10820
9.0%
S7133
 
6.0%
G7133
 
6.0%
ò7133
 
6.0%
H6005
 
5.0%
6005
 
5.0%
Other values (3)14445
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)119666
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
à17953
15.0%
17953
15.0%
i13138
11.0%
n11948
10.0%
N10820
9.0%
S7133
 
6.0%
G7133
 
6.0%
ò7133
 
6.0%
H6005
 
5.0%
6005
 
5.0%
Other values (3)14445
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)119666
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
à17953
15.0%
17953
15.0%
i13138
11.0%
n11948
10.0%
N10820
9.0%
S7133
 
6.0%
G7133
 
6.0%
ò7133
 
6.0%
H6005
 
5.0%
6005
 
5.0%
Other values (3)14445
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)119666
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
à17953
15.0%
17953
15.0%
i13138
11.0%
n11948
10.0%
N10820
9.0%
S7133
 
6.0%
G7133
 
6.0%
ò7133
 
6.0%
H6005
 
5.0%
6005
 
5.0%
Other values (3)14445
12.1%

destination
Categorical

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Nha Trang - Khánh Hòa
1370 
Quảng Ninh
 
1117
Ninh Bình
 
1017
Gia Lai
 
952
Bình Thuận
 
943
Other values (16)
12554 

Length

Max length21
Median length15
Mean length10.68774
Min length6

Characters and Unicode

Total characters191,877
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGia Lai
2nd rowGia Lai
3rd rowGia Lai
4th rowGia Lai
5th rowGia Lai

Common Values

ValueCountFrequency (%)
Nha Trang - Khánh Hòa1370
 
7.6%
Quảng Ninh1117
 
6.2%
Ninh Bình1017
 
5.7%
Gia Lai952
 
5.3%
Bình Thuận943
 
5.3%
Bà Rịa-Vũng Tàu920
 
5.1%
Phú Yên920
 
5.1%
Hải Phòng909
 
5.1%
Thừa Thiên-Huế908
 
5.1%
Đà Lạt - Lâm Đồng907
 
5.1%
Other values (11)7990
44.5%

Length

2025-11-16T16:52:05.394609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3056
 
6.6%
quảng2916
 
6.3%
ninh2840
 
6.2%
bình1960
 
4.3%
thuận1649
 
3.6%
1376
 
3.0%
nha1370
 
3.0%
khánh1370
 
3.0%
hòa1370
 
3.0%
trang1370
 
3.0%
Other values (33)26717
58.1%

Most occurring characters

ValueCountFrequency (%)
28041
14.6%
n19903
 
10.4%
h16613
 
8.7%
a13819
 
7.2%
i9983
 
5.2%
g9625
 
5.0%
N7461
 
3.9%
T6516
 
3.4%
u6393
 
3.3%
H5324
 
2.8%
Other values (40)68199
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)191877
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
28041
14.6%
n19903
 
10.4%
h16613
 
8.7%
a13819
 
7.2%
i9983
 
5.2%
g9625
 
5.0%
N7461
 
3.9%
T6516
 
3.4%
u6393
 
3.3%
H5324
 
2.8%
Other values (40)68199
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)191877
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
28041
14.6%
n19903
 
10.4%
h16613
 
8.7%
a13819
 
7.2%
i9983
 
5.2%
g9625
 
5.0%
N7461
 
3.9%
T6516
 
3.4%
u6393
 
3.3%
H5324
 
2.8%
Other values (40)68199
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)191877
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
28041
14.6%
n19903
 
10.4%
h16613
 
8.7%
a13819
 
7.2%
i9983
 
5.2%
g9625
 
5.0%
N7461
 
3.9%
T6516
 
3.4%
u6393
 
3.3%
H5324
 
2.8%
Other values (40)68199
35.5%

rating_safety
Real number (ℝ)

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.583947
Minimum2.3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:05.487599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile3.9
Q14.5
median4.7
Q34.8
95-th percentile5
Maximum5
Range2.7
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.36319763
Coefficient of variation (CV)0.079232511
Kurtosis4.6621875
Mean4.583947
Median Absolute Deviation (MAD)0.2
Skewness-1.7882467
Sum82295.6
Variance0.13191252
MonotonicityNot monotonic
2025-11-16T16:52:05.580631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4.73552
19.8%
4.82928
16.3%
4.51923
10.7%
51908
10.6%
4.91643
9.2%
4.61601
8.9%
4.31021
 
5.7%
4.4748
 
4.2%
4.2733
 
4.1%
4.1595
 
3.3%
Other values (13)1301
 
7.2%
ValueCountFrequency (%)
2.316
 
0.1%
2.46
 
< 0.1%
384
 
0.5%
3.114
 
0.1%
3.264
 
0.4%
3.3141
0.8%
3.452
 
0.3%
3.531
 
0.2%
3.665
 
0.4%
3.7257
1.4%
ValueCountFrequency (%)
51908
10.6%
4.91643
9.2%
4.82928
16.3%
4.73552
19.8%
4.61601
8.9%
4.51923
10.7%
4.4748
 
4.2%
4.31021
 
5.7%
4.2733
 
4.1%
4.1595
 
3.3%

rating_info_accuracy
Real number (ℝ)

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4525706
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:05.674607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.6
Q14.2
median4.6
Q34.8
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.46560532
Coefficient of variation (CV)0.10457
Kurtosis5.4302731
Mean4.4525706
Median Absolute Deviation (MAD)0.2
Skewness-1.8396043
Sum79937
Variance0.21678832
MonotonicityNot monotonic
2025-11-16T16:52:05.777030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4.72941
16.4%
4.62316
12.9%
4.81966
11.0%
4.91566
8.7%
4.51354
7.5%
4.41220
6.8%
51091
 
6.1%
4.1956
 
5.3%
4.3915
 
5.1%
3.9783
 
4.4%
Other values (19)2845
15.8%
ValueCountFrequency (%)
117
 
0.1%
24
 
< 0.1%
2.322
 
0.1%
2.519
 
0.1%
2.622
 
0.1%
2.7126
0.7%
2.887
0.5%
2.98
 
< 0.1%
396
0.5%
3.114
 
0.1%
ValueCountFrequency (%)
51091
 
6.1%
4.91566
8.7%
4.81966
11.0%
4.72941
16.4%
4.62316
12.9%
4.51354
7.5%
4.41220
6.8%
4.3915
 
5.1%
4.2746
 
4.2%
4.1956
 
5.3%

rating_info_completeness
Real number (ℝ)

Distinct28
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5107949
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:05.870020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.7
Q14.3
median4.6
Q34.8
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.4230589
Coefficient of variation (CV)0.093788104
Kurtosis6.0440824
Mean4.5107949
Median Absolute Deviation (MAD)0.2
Skewness-2.0032813
Sum80982.3
Variance0.17897883
MonotonicityNot monotonic
2025-11-16T16:52:05.966034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4.73360
18.7%
4.82535
14.1%
4.62208
12.3%
4.91724
9.6%
4.51568
8.7%
4.31280
 
7.1%
51045
 
5.8%
4.1884
 
4.9%
4.4747
 
4.2%
4.2576
 
3.2%
Other values (18)2026
11.3%
ValueCountFrequency (%)
17
 
< 0.1%
2.46
 
< 0.1%
2.524
 
0.1%
2.682
0.5%
2.756
0.3%
2.814
 
0.1%
2.927
 
0.2%
3116
0.6%
3.115
 
0.1%
3.241
 
0.2%
ValueCountFrequency (%)
51045
 
5.8%
4.91724
9.6%
4.82535
14.1%
4.73360
18.7%
4.62208
12.3%
4.51568
8.7%
4.4747
 
4.2%
4.31280
 
7.1%
4.2576
 
3.2%
4.1884
 
4.9%

rating_staff_attitude
Real number (ℝ)

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4859968
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:06.069851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.7
Q14.3
median4.6
Q34.8
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.42575818
Coefficient of variation (CV)0.094908268
Kurtosis7.8655685
Mean4.4859968
Median Absolute Deviation (MAD)0.2
Skewness-1.9774285
Sum80537.1
Variance0.18127003
MonotonicityNot monotonic
2025-11-16T16:52:06.165875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4.62969
16.5%
4.72271
12.6%
4.51881
10.5%
4.81827
10.2%
4.91518
8.5%
51401
7.8%
4.41178
 
6.6%
4.2901
 
5.0%
4.3854
 
4.8%
4814
 
4.5%
Other values (15)2339
13.0%
ValueCountFrequency (%)
124
 
0.1%
2.766
0.4%
2.875
0.4%
2.95
 
< 0.1%
375
0.4%
3.129
 
0.2%
3.2106
0.6%
3.326
 
0.1%
3.4110
0.6%
3.5117
0.7%
ValueCountFrequency (%)
51401
7.8%
4.91518
8.5%
4.81827
10.2%
4.72271
12.6%
4.62969
16.5%
4.51881
10.5%
4.41178
 
6.6%
4.3854
 
4.8%
4.2901
 
5.0%
4.1619
 
3.4%

rating_comfort
Real number (ℝ)

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4351362
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:06.264637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.6
Q14.2
median4.6
Q34.7
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.46482656
Coefficient of variation (CV)0.10480548
Kurtosis5.8150221
Mean4.4351362
Median Absolute Deviation (MAD)0.2
Skewness-1.8275444
Sum79624
Variance0.21606373
MonotonicityNot monotonic
2025-11-16T16:52:06.360600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4.72601
14.5%
4.62355
13.1%
4.51756
9.8%
4.81735
9.7%
4.41603
8.9%
51261
7.0%
4.91162
6.5%
41041
 
5.8%
4.3879
 
4.9%
4.2857
 
4.8%
Other values (19)2703
15.1%
ValueCountFrequency (%)
116
 
0.1%
1.716
 
0.1%
220
 
0.1%
2.46
 
< 0.1%
2.524
 
0.1%
2.679
0.4%
2.834
 
0.2%
2.915
 
0.1%
3139
0.8%
3.124
 
0.1%
ValueCountFrequency (%)
51261
7.0%
4.91162
6.5%
4.81735
9.7%
4.72601
14.5%
4.62355
13.1%
4.51756
9.8%
4.41603
8.9%
4.3879
 
4.9%
4.2857
 
4.8%
4.1275
 
1.5%

rating_service_quality
Real number (ℝ)

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4015262
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:06.457150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.6
Q14.2
median4.5
Q34.7
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.4767195
Coefficient of variation (CV)0.10830777
Kurtosis3.4344438
Mean4.4015262
Median Absolute Deviation (MAD)0.3
Skewness-1.5189005
Sum79020.6
Variance0.22726149
MonotonicityNot monotonic
2025-11-16T16:52:06.556733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4.72579
14.4%
4.61998
11.1%
4.41834
10.2%
4.51628
9.1%
4.91487
8.3%
4.81465
8.2%
4.2989
 
5.5%
5956
 
5.3%
4740
 
4.1%
3.8710
 
4.0%
Other values (20)3567
19.9%
ValueCountFrequency (%)
17
 
< 0.1%
217
 
0.1%
2.316
 
0.1%
2.433
 
0.2%
2.55
 
< 0.1%
2.677
0.4%
2.737
 
0.2%
2.824
 
0.1%
2.9123
0.7%
3116
0.6%
ValueCountFrequency (%)
5956
 
5.3%
4.91487
8.3%
4.81465
8.2%
4.72579
14.4%
4.61998
11.1%
4.51628
9.1%
4.41834
10.2%
4.3709
 
3.9%
4.2989
 
5.5%
4.1672
 
3.7%

rating_punctuality
Real number (ℝ)

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6133292
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:06.647112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14.6
median4.7
Q34.9
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.46863288
Coefficient of variation (CV)0.10158236
Kurtosis23.298916
Mean4.6133292
Median Absolute Deviation (MAD)0.1
Skewness-4.0376156
Sum82823.1
Variance0.21961677
MonotonicityNot monotonic
2025-11-16T16:52:06.746412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4.73944
22.0%
4.83276
18.2%
52274
12.7%
4.92216
12.3%
4.61777
9.9%
4.41043
 
5.8%
4.3933
 
5.2%
4.5770
 
4.3%
4496
 
2.8%
4.2236
 
1.3%
Other values (16)988
 
5.5%
ValueCountFrequency (%)
1106
0.6%
2.126
 
0.1%
2.28
 
< 0.1%
2.314
 
0.1%
2.660
 
0.3%
2.914
 
0.1%
3155
0.9%
3.222
 
0.1%
3.3117
0.7%
3.42
 
< 0.1%
ValueCountFrequency (%)
52274
12.7%
4.92216
12.3%
4.83276
18.2%
4.73944
22.0%
4.61777
9.9%
4.5770
 
4.3%
4.41043
 
5.8%
4.3933
 
5.2%
4.2236
 
1.3%
4.1127
 
0.7%

rating_overall
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4244862
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:06.844412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.4
Q14.2
median4.6
Q34.8
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.49362784
Coefficient of variation (CV)0.11156727
Kurtosis5.8400381
Mean4.4244862
Median Absolute Deviation (MAD)0.2
Skewness-1.873117
Sum79432.8
Variance0.24366844
MonotonicityNot monotonic
2025-11-16T16:52:06.939637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4.82863
15.9%
4.72503
13.9%
4.62209
12.3%
4.41531
8.5%
4.51359
7.6%
51020
 
5.7%
3.8960
 
5.3%
4.9945
 
5.3%
4.2855
 
4.8%
4803
 
4.5%
Other values (21)2905
16.2%
ValueCountFrequency (%)
05
 
< 0.1%
116
 
0.1%
217
 
0.1%
2.210
 
0.1%
2.316
 
0.1%
2.518
 
0.1%
2.67
 
< 0.1%
2.790
0.5%
2.893
0.5%
2.980
0.4%
ValueCountFrequency (%)
51020
 
5.7%
4.9945
 
5.3%
4.82863
15.9%
4.72503
13.9%
4.62209
12.3%
4.51359
7.6%
4.41531
8.5%
4.3670
 
3.7%
4.2855
 
4.8%
4.1671
 
3.7%

reviewer_count
Real number (ℝ)

Distinct443
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2048.754
Minimum0
Maximum17351
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:07.041872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q1106
median472
Q32051
95-th percentile8733
Maximum17351
Range17351
Interquartile range (IQR)1945

Descriptive statistics

Standard deviation3519.7191
Coefficient of variation (CV)1.7179804
Kurtosis6.855888
Mean2048.754
Median Absolute Deviation (MAD)449
Skewness2.6121289
Sum36781280
Variance12388423
MonotonicityNot monotonic
2025-11-16T16:52:07.148045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169408
 
2.3%
81406
 
2.3%
11284
 
1.6%
163244
 
1.4%
6241
 
1.3%
106226
 
1.3%
8632188
 
1.0%
370180
 
1.0%
89178
 
1.0%
200177
 
1.0%
Other values (433)15421
85.9%
ValueCountFrequency (%)
05
 
< 0.1%
197
0.5%
295
 
0.5%
318
 
0.1%
415
 
0.1%
589
 
0.5%
6241
1.3%
7139
0.8%
838
 
0.2%
951
 
0.3%
ValueCountFrequency (%)
17351138
0.8%
17341104
0.6%
1733152
 
0.3%
13965172
1.0%
13961120
0.7%
1395462
 
0.3%
139514
 
< 0.1%
1308465
 
0.4%
1307855
 
0.3%
1307420
 
0.1%

number_of_seat
Real number (ℝ)

Distinct36
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.475185
Minimum4
Maximum241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:07.257838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7
Q111
median22
Q334
95-th percentile40
Maximum241
Range237
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.4868
Coefficient of variation (CV)0.60007516
Kurtosis90.002147
Mean22.475185
Median Absolute Deviation (MAD)11
Skewness5.724673
Sum403497
Variance181.89378
MonotonicityNot monotonic
2025-11-16T16:52:07.358829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
243539
19.7%
342817
15.7%
71910
10.6%
91878
10.5%
221560
8.7%
11983
 
5.5%
21750
 
4.2%
16653
 
3.6%
32599
 
3.3%
40588
 
3.3%
Other values (26)2676
14.9%
ValueCountFrequency (%)
49
 
0.1%
6110
 
0.6%
71910
10.6%
817
 
0.1%
91878
10.5%
10349
 
1.9%
11983
5.5%
12102
 
0.6%
1364
 
0.4%
1529
 
0.2%
ValueCountFrequency (%)
24124
 
0.1%
4647
 
0.3%
45277
1.5%
44158
 
0.9%
4354
 
0.3%
4227
 
0.2%
41153
 
0.9%
40588
3.3%
38176
 
1.0%
36281
1.6%

duration_minutes
Real number (ℝ)

Distinct231
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean371.41536
Minimum30
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size796.6 KiB
2025-11-16T16:52:07.635358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile90
Q1150
median330
Q3525
95-th percentile840
Maximum1500
Range1470
Interquartile range (IQR)375

Descriptive statistics

Standard deviation252.62814
Coefficient of variation (CV)0.68017687
Kurtosis0.46110505
Mean371.41536
Median Absolute Deviation (MAD)180
Skewness0.96680861
Sum6668020
Variance63820.976
MonotonicityNot monotonic
2025-11-16T16:52:07.747358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1501440
 
8.0%
1801060
 
5.9%
120687
 
3.8%
90631
 
3.5%
390502
 
2.8%
360472
 
2.6%
210370
 
2.1%
80351
 
2.0%
480328
 
1.8%
135323
 
1.8%
Other values (221)11789
65.7%
ValueCountFrequency (%)
3027
 
0.2%
3521
 
0.1%
4514
 
0.1%
5061
 
0.3%
60211
1.2%
6514
 
0.1%
7042
 
0.2%
7583
 
0.5%
80351
2.0%
852
 
< 0.1%
ValueCountFrequency (%)
15007
 
< 0.1%
12607
 
< 0.1%
118522
 
0.1%
117023
 
0.1%
11607
 
< 0.1%
115537
 
0.2%
115014
 
0.1%
1140102
0.6%
11351
 
< 0.1%
112522
 
0.1%

Interactions

2025-11-16T16:52:00.885160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:45.479115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:46.656185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:47.951338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:49.082016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:50.293876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:51.737442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:52.955371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:54.347689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:55.574261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:56.949630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:58.381583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:59.679133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:00.979157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:45.572109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:46.746616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:48.039327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:49.175017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:50.553873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:51.833913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:53.069349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:54.439666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:55.675770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:57.033254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:58.478593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:59.786147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:01.065917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:45.656284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:46.823404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:48.121328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:49.264484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:50.643900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:51.923945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:53.157361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:54.530031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:55.811759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:57.114238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:58.571578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:59.889124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:01.160454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:45.744286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:47.068162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:48.200338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:49.351666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:50.734891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:52.018640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:53.248464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:54.622381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:55.913793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:57.197233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:58.659566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:59.992126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:01.423457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:45.834887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-16T16:51:52.112650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:53.344052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:54.714452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:56.025761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:57.283254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:58.754575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:00.089789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:01.518475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:45.928709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:47.245162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:48.376340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:49.540673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:50.925893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-16T16:51:53.438621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-16T16:51:56.137757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:57.576424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:58.850568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:00.180519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:01.613482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:46.021714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-16T16:51:57.685340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:58.947573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:00.271288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-16T16:51:49.732688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:51.121044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:52.398632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:53.629622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:55.003426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:56.362759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:57.780937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:59.043592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:00.361729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:01.803252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:46.209875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:47.508759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:48.644923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:49.826668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:51.216744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:52.492659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:53.725622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:55.098452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:56.465769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:57.917932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:59.137094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-16T16:51:52.589638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:53.985603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:55.192428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:56.571634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-16T16:51:47.765350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:48.907708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:50.107589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:51.515423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:52.773642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:54.166622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:55.374426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:56.760625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:58.204943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:59.462438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:00.714272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:02.171347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:46.566206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:47.864319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:48.993601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:50.199523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:51.641412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:52.861660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:54.254669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:55.469547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:56.855635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:58.288930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:51:59.561749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-16T16:52:00.795159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Missing values

2025-11-16T16:52:02.333356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-16T16:52:02.552974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

company_namedeparture_timepickup_pointarrival_timedropoff_pointprice_originalprice_discounteddeparture_datestart_pointdestinationrating_safetyrating_info_accuracyrating_info_completenessrating_staff_attituderating_comfortrating_service_qualityrating_punctualityrating_overallreviewer_countnumber_of_seatduration_minutes
0Hoàng Thuỷ18:45:00Bến xe05:45Bến xe3500003000002025-11-12Sài GònGia Lai4.84.74.74.74.64.64.94.7348534660
1Sinh Diên Hồng17:30:00Bến xe04:15Bến xe3700002960002025-11-12Sài GònGia Lai4.74.74.84.84.64.64.94.623734645
2Phong Phú20:00:00Văn phòng07:45Văn phòng5300003990002025-11-12Sài GònGia Lai4.54.54.64.54.44.44.74.5867724705
3Kính Diên Hồng18:40:00Bến xe05:40Bến xe35000002025-11-12Sài GònGia Lai4.74.64.74.64.54.54.54.6113534660
4Đức Đạt20:10:00Bến xe06:35Bến xe3500003000002025-11-12Sài GònGia Lai4.64.54.64.54.54.54.94.7102734625
5Vương Tấn Dũng16:45:00Văn phòng06:00Other3800003200002025-11-12Sài GònGia Lai4.84.84.84.84.84.84.94.838634795
6Thuận Tiến18:50:00Bến xe07:15Bến xe35000002025-11-12Sài GònGia Lai4.74.74.84.64.64.64.94.7495734745
7Bảy Lang18:30:00Bến xe05:30Bến xe35000002025-11-12Sài GònGia Lai4.84.64.64.74.54.64.64.727934660
8Tấn Hưng17:30:00Bến xe06:55Văn phòng6500003990002025-11-12Sài GònGia Lai4.54.34.54.54.54.44.04.410424805
9Gia Phú - Gia Lai18:45:00Bến xe05:45Bến xe5500005000002025-11-12Sài GònGia Lai4.54.34.44.54.34.25.04.410822660
company_namedeparture_timepickup_pointarrival_timedropoff_pointprice_originalprice_discounteddeparture_datestart_pointdestinationrating_safetyrating_info_accuracyrating_info_completenessrating_staff_attituderating_comfortrating_service_qualityrating_punctualityrating_overallreviewer_countnumber_of_seatduration_minutes
2644Cúc Tùng19:45:00Bến xe05:45Bến xe5030003300002025-11-19Đà NẵngNha Trang - Khánh Hòa4.13.94.14.04.03.84.13.8286121600
2645Cúc Tùng19:45:00Bến xe07:00Văn phòng5030003300002025-11-19Đà NẵngNha Trang - Khánh Hòa4.13.94.14.04.03.84.13.8286121675
2646Tân Quang Dũng11:30:00Văn phòng22:00Other45000002025-11-19Đà NẵngNha Trang - Khánh Hòa4.13.94.04.03.93.84.33.8238632630
2647Tân Quang Dũng18:52:00Văn phòng05:22Other100000002025-11-19Đà NẵngNha Trang - Khánh Hòa4.13.94.04.03.93.84.33.8238622630
2648Tân Quang Dũng19:00:00Văn phòng05:30Other45000002025-11-19Đà NẵngNha Trang - Khánh Hòa4.13.94.04.03.93.84.33.8238632630
2649Cúc Tùng18:00:00Bến xe05:15Văn phòng6260003300002025-11-19Đà NẵngNha Trang - Khánh Hòa4.13.94.14.04.03.84.13.8286121675
2650Tân Quang Dũng19:01:00Văn phòng05:31Other45000002025-11-19Đà NẵngNha Trang - Khánh Hòa4.13.94.04.03.93.84.33.8238622630
2651Tân Quang Dũng18:45:00Văn phòng02:45Other45000002025-11-19Đà NẵngNha Trang - Khánh Hòa4.13.94.04.03.93.84.33.8238622480
2652Tân Quang Dũng18:45:00Văn phòng02:50Bến xe45000002025-11-19Đà NẵngNha Trang - Khánh Hòa4.13.94.04.03.93.84.33.8238622485
2653Tân Quang Dũng18:36:00Văn phòng06:06Other45000002025-11-19Đà NẵngNha Trang - Khánh Hòa4.13.94.04.03.93.84.33.8238622690